Multiobjective Hybrid Genetic Algorithm for reverse logistics network design of inventory systems with backordering

The reverse logistics is the process flow of used-products that are collected to be reproduced so that they can be sold again to customers after some processing. Within this perspective, we formulated a mathematical model of the reuse system as a reverse logistics with two objectives functions: one is minimizing cost of reverse logistics network problem, (i.e. transportation cost, disposal cost, purchase cost and inventory holding cost) another is just-in-time delivery, and minimizes the costs of backorders and inventories in manufacturer in all periods. This paper proposes a new multiobjective hybrid genetic algorithm approach, and shows how the performance of multiobjective genetic algorithm can be improved by hybridization with Fuzzy Logic Control (FLC). In the experimental results comparing CPLEX, pri-awGA (priority-based adaptive weight Genetic Algorithm) and mo-hGA (multiobjective Hybrid Genetic Algorithm), we demonstrated the effectiveness of mo-hGA such as shortness of computational time and better solutions.